frontend/utils/api_client.py

"""
utils/api_client.py
--------------------
Centralized, cached API wrappers.

HF Spaces compatible: reads API_URL from environment variable so the
same code works locally (localhost:8000) and on HuggingFace (localhost:8000
since both services run in the same container).
"""

import os
import requests
import pandas as pd
import streamlit as st

# HF Spaces: backend always on localhost:8000 inside the container
API = os.environ.get("API_URL", "http://localhost:8000")
TIMEOUT = 15


@st.cache_data(ttl=300)
def _get(endpoint: str, params: dict | None = None):
    """Raw cached GET — returns JSON or None on any error."""
    try:
        r = requests.get(f"{API}{endpoint}", params=params or {}, timeout=TIMEOUT)
        r.raise_for_status()
        return r.json()
    except requests.exceptions.ConnectionError:
        return None
    except requests.exceptions.Timeout:
        return None
    except Exception:
        return None


def _df(data) -> pd.DataFrame:
    if not data:
        return pd.DataFrame()
    if isinstance(data, list):
        return pd.DataFrame(data)
    if isinstance(data, dict):
        return pd.DataFrame([data])
    return pd.DataFrame()


# ── Health ─────────────────────────────────────────────────────────────────────
def is_online() -> bool:
    try:
        requests.get(f"{API}/health", timeout=5)
        return True
    except Exception:
        return False


# ── /districts/* ───────────────────────────────────────────────────────────────
def fetch_stats() -> dict:
    return _get("/districts/stats") or {}


def fetch_states() -> list[str]:
    return _get("/districts/states") or []


def fetch_districts(state: str) -> list[str]:
    return _get("/districts/list", {"state": state}) or []


def fetch_district_history(state: str, district: str) -> pd.DataFrame:
    return _df(_get("/districts/history", {"state": state, "district": district}))


def fetch_top_districts(
    state: str | None = None,
    metric: str = "person_days_lakhs",
    n: int = 12,
) -> pd.DataFrame:
    params = {"metric": metric, "n": n}
    if state:
        params["state"] = state
    return _df(_get("/districts/top", params))


def fetch_yearly_trend(state: str | None = None) -> pd.DataFrame:
    params = {"state": state} if state else {}
    return _df(_get("/districts/trend", params))


# ── /predictions/* ─────────────────────────────────────────────────────────────
def fetch_predictions(
    state: str | None = None,
    district: str | None = None,
    year: int | None = None,
) -> pd.DataFrame:
    params = {}
    if state:    params["state"]    = state
    if district: params["district"] = district
    if year:     params["year"]     = year
    return _df(_get("/predictions/", params))


# ── /optimizer/* ───────────────────────────────────────────────────────────────
def fetch_optimizer_results(state: str | None = None) -> pd.DataFrame:
    params = {"state": state} if state else {}
    return _df(_get("/optimizer/results", params))


def run_optimizer_live(
    state: str | None = None,
    budget_scale: float = 1.0,
    min_fraction: float = 0.40,
    max_fraction: float = 2.50,
) -> dict | None:
    payload = {
        "state":        state,
        "budget_scale": budget_scale,
        "min_fraction": min_fraction,
        "max_fraction": max_fraction,
    }
    try:
        r = requests.post(f"{API}/optimizer/run", json=payload, timeout=60)
        r.raise_for_status()
        return r.json()
    except requests.exceptions.ConnectionError:
        st.error("Cannot reach API — backend may still be starting up, refresh in a moment.")
        return None
    except Exception as e:
        st.error(f"Optimizer error: {e}")
        return None